Papers
4
Total Citations
30
H-Index
3
About
Redouane Ayad is a researcher at the forefront of intelligent control systems and autonomous robotics, with a particular focus on unmanned aerial vehicles (UAVs) and mobile robots. His work bridges evolutionary computation and advanced control theory, most notably demonstrated in his highly cited 2019 paper on evolutionary autopilot design for quadrotor UAVs using genetic algorithms (19 citations). Ayad has made significant contributions to airport safety through his development of an automated Foreign Object Debris (FOD) detection system using UAVs, addressing critical risks to aircraft and personnel. His research extends to fractional-order control, where he proposed innovative PI^λ D^μ controllers for trajectory tracking in non-holonomic mobile robots. Most recently, Ayad has advanced industrial robotics by developing optimal trajectory planning methods that minimize time, jerk, and energy consumption, incorporating LSTM neural networks for energy profile modeling. His work demonstrates a consistent focus on practical, real-world applications—from airport safety to industrial energy efficiency—making him a notable contributor to the fields of autonomous systems, control engineering, and robotics optimization.
Research Focus
Key Achievements
Top Papers
- 1Evolutionary Autopilot Design Approach for UAV Quadrotor by Using GA19 citations · 2019
- 2Automated Foreign Object Debris Detection System based on UAV5 citations · 2022
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